West Nile virus transmission potential in Portugal

It is unclear whether West Nile virus (WNV) circulates endemically in Portugal. Despite the country’s adequate climate for transmission, Portugal has only reported four human WNV infections so far. We performed a review of WNV-related data (1966–2020), explored mosquito (2016–2019) and land type distributions (1992–2019), and used climate data (1981–2019) to estimate WNV transmission suitability in Portugal. Serological and molecular evidence of WNV circulation from animals and vectors was largely restricted to the south. Land type and climate-driven transmission suitability distributions, but not the distribution of WNV-capable vectors, were compatible with the North-South divide present in serological and molecular evidence of WNV circulation. Our study offers a comprehensive, data-informed perspective and review on the past epidemiology, surveillance and climate-driven transmission suitability of WNV in Portugal, highlighting the south as a subregion of importance. Given the recent WNV outbreaks across Europe, our results support a timely change towards local, active surveillance.

Excavated farmland with plastic mulching as a strategy in saving water and controlling soil salinization in dryland agricultural areas

Water shortage and soil salinization in gully farmland comprising sediment deposited farmland (SF) and excavated farmland (EF) have become a widespread concern in the loess hilly region. A two-year field experiment was conducted to assess the soil water content (SWC) and salt content (SSC) and their effect on the spring maize yield and water use efficiency in SF and EF. Eight treatments comprising flat cropping without mulching (1), ridge planting without mulching (2), ridge planting with plastic mulching (3), and ridge planting with straw mulching (4) were tested in the SF and EF plots, respectively. The results showed that the yield was higher in SF than EF, whereas the water use efficiency was significantly higher in EF because the bottom water flux was 117.4% higher in SF than EF (P < 0.01). A significant positive correlation was found between the average SWC and yield (P < 0.01), thereby indicating that the yield was severely limited by the SWC. Thus, the higher water use efficiency in EF has important implications for alleviating water scarcity during agricultural production in this region. The risk of soil salinization was decreased greatly by treatment 3 where the SSC was decreased in EF and SF were 0.09 g kg–1 and 0.08 g kg–1, respectively. In addition, treatment 3 had the most significant impacts on the yield and water use efficiency. Our study provided appropriate land type and effective tillage measure for the sustainable development in dryland agricultural areas.

Agroecological land typification

The minimum required information and the sequence of agroecological land type allocation on the land management territory of the experimental station (ES) located in the forest-steppe of the Priob'ye region of Novosibirsk is described. Two agroecological land types are distinguished: the first type (upland lands) is represented by leached chernozem in combination with common, podzolized and dark-grey forest soils; the second type (slightly erosive lands) is represented by leached chernozem in combination with dark-grey forest soils. Soil cover of ES in both types of lands is represented by leached chernozem (Lch-2-2s), the share of which for the first type is 75.26%, for the second - 76.26% of the total area of the types. The first agroecological land type is characterized by a range of heights from 134 to 165 m. The working areas are located on the slopes between 0 and 3 degrees in relation to the terrain angle. Vertical dissection of the relief averages 1.3 m, horizontal dissection by erosion forms is 0.8 km/km2. The second type of land is characterized by an elevation of 113 to 137 meters above sea level and a slope of 1 to 4 degrees. Vertical dissection of the terrain averages 1.7 m, horizontal dissection by erosion forms 0.9 km/km2. Typification was carried out with the help of a generated Digital Land Use Model (DLM) of the ES based on the analysis of geographical information, remote sensing materials (ERS) and cadastral map. The DLM consists of the following geo-information layers: topography, land cover, digital elevation model (DEM), working areas. The DEM includes information on slope steepness and exposure, vertical and horizontal dissection.

Positive Unlabelled Learning for Satellite Images’Time Series Analysis: An Application to Cereal and Forest Mapping

Applications in which researchers aim to extract a single land type from remotely sensed data are quite common in practical scenarios: extract the urban footprint to make connections with socio-economic factors; map the forest extent to subsequently retrieve biophysical variables and detect a particular crop type to successively calibrate and deploy yield prediction models. In this scenario, the (positive) targeted class is well defined, while the negative class is difficult to describe. This one-class classification setting is also referred to as positive unlabelled learning (PUL) in the general field of machine learning. To deal with this challenging setting, when satellite image time series data are available, we propose a new framework named positive and unlabelled learning of satellite image time series (PUL-SITS). PUL-SITS involves two different stages: In the first one, a recurrent neural network autoencoder is trained to reconstruct only positive samples with the aim to higight reliable negative ones. In the second stage, both labelled and unlabelled samples are exploited in a semi-supervised manner to build the final binary classification model. To assess the quality of our approach, experiments were carried out on a real-world benchmark, namely Haute-Garonne, located in the southwest area of France. From this study site, we considered two different scenarios: a first one in which the process has the objective to map Cereals/Oilseeds cover versus the rest of the land cover classes and a second one in which the class of interest is the Forest land cover. The evaluation was carried out by comparing the proposed approach with recent competitors to deal with the considered positive and unlabelled learning scenarios.

Remote Sensing Image Land Classification Based on Deep Learning

Aiming at the problems of high-resolution remote sensing images with many features and low classification accuracy using a single feature description, a remote sensing image land classification model based on deep learning from the perspective of ecological resource utilization is proposed. Firstly, the remote sensing image obtained by Gaofen-1 satellite is preprocessed, including multispectral data and panchromatic data. Then, the color, texture, shape, and local features are extracted from the image data, and the feature-level image fusion method is used to associate these features to realize the fusion of remote sensing image features. Finally, the fused image features are input into the trained depth belief network (DBN) for processing, and the land type is obtained by the Softmax classifier. Based on the Keras and TensorFlow platform, the experimental analysis of the proposed model shows that it can clearly classify all land types, and the overall accuracy, F1 value, and reasoning time of the classification results are 97.86%, 87.25%, and 128 ms, respectively, which are better than other comparative models.

Direct impact of solar farm deployment on surface longwave radiation

Motivated by a previous study of using the Moderate Resolution Imaging Spectroradiometers (MODIS) observations to quantify changes in surface shortwave spectral reflectances caused by six solar farms in the southwest United States, here we used a similar method to study the longwave effects of the same six solar farms, with emphases on surface emissivities and land surface temperature (LST). Two MODIS surface products were examined: one relying on generalized split-window algorithm while assuming emissivities from land cover classifications (MYD11A2), the other based on Temperature Emissivity Separation algorithm capable of dynamically retrieving emissivities (MYD21A2). Both products suggest that, compared to adjacent regions without changes before and after solar farm constructions, the solar farm sites have reduced outgoing radiances in three MODIS infrared window channels. Such reduction in upward longwave radiation is consistent with previous in-situ measurements. The MYD11A2 results show constant emissivities before and after solar farm constructions because its land type classification algorithm is not aware of the presence of solar farms. The estimated daytime and nighttime LST reduction due to solar farm deployment are ~1-4K and ~0.2-0.9K, respectively. The MYD21A2 results indicate a decrease in Band 31 (10.78-11.28 µm) emissivity up to −0.01 and little change in Band 32 (11.77-12.27 µm) emissivity. The LST decreases in the MYD21A2 is slightly smaller than its counterpart in the MYD11A2. Laboratory and in-situ measurements indicate the longwave emissivity of solar panels can be as low as 0.83, considerably smaller than MODIS retrieved surface emissivity over the solar farm sites. The contribution of exposed and shaded ground within the solar farm to the upward longwave radiation needs to be considered to fully explain the results. A synthesis of MODIS observations and published in-situ measurements is presented. Implication for parameterizing such solar farm longwave effect in the climate models is also discussed.

Factors Influencing Participation of Farmer in Contract Farming in Narsingdi District of Bangladesh

Aims: Contract farming (CF) has been used extensively to integrate agricultural value chain both in the developed and developing countries. Participation in CF is associated with increased farm productivity and farmer income. Therefore, the purpose of this study was to analyze socio-economic factors affecting farmer’s participation in contract farming. Study Design: The selected 15 socio-economic factors were used to determine the impact on farmer’s participation in contract farming. Besides, the socio-demographic profile of the farmers in the study area was discussed. Place and Duration of Study: The study was conducted upon contract and non-contract farmers of Shibpur upazila under Narsingdi district of Bangladesh. The duration of the study was from July, 2019 to December, 2020. Methodology: The study used nationally-representative data of smallholder vegetable farmers in Shibpur upazila of Narsingdi district. The data were collected from 75 contract farmer and 125 non-contract farmer of Shibpur upazila. Binary logistic regression was used to analyze fifteen factors that potentially affected farmers’ decision to participate in CF and descriptive statistics were used to analyze the socio-demographic profile of the farmer. Results: The study found that farmers education, wife’s occupation, family size, labor, fertilizer use, training, savings and income were the significant factors in the model and farmers occupation, other family member's occupation, income source, land type, size of land, experience and storage place were non-significant predictors using P<0.01 and P<0.05 threshold. Conclusion: Farmer’s education, female head's occupation, family size, land type, size of land holdings, labor use, type of fertilizers being used, training or technical knowledge and average monthly income of the respondents had a positive influence on farmer’s decision.

Land typology by L. G. Ramenskiy and eunis habitat classification (retrospective view)

The rapid rate of decline in the Earth’s biodiversity under the influence of direct and indirect anthropogenic pressure makes it necessary to develop the scientific foundations for its conservation at all levels of life. Ecologists have come to understand that the best way to ensure the conservation of populations of organisms and their communities is to preserve the environment in which they live. The countries of the European Community, where special programs have been developed since mid 1980s, have shown the greatest activity in preserving environmental conditions. Currently, the «European Union Nature Information System» (EUNIS) has become the most popular among such programs. Habitat is a central concept in EUNIS. For the purposes of EUNIS, habitat is defined asa place where plants or animals normally live, characterized primarily by its physical features (topography, plant or animal physiognomy, soil characteristics, climate, water quality etc.) and secondarily by the species of plants and animals that live there (Davies et al., 2004). Most often, habitat is considered to be synonym of the term biotope. The EUNIS biotope classification would correspond to the ecosystem classification if heterotrophic components were largely present in it. However, at present, these organisms, are not used for classification of terrestrial ecosystems. The latter (especially benthos) are important in the characterization of marine habitat types. The author does not deny the extreme importance of the EUNIS habitat classification for ecological science and solving problems of nature conservation. He is only sure that the concept of habitat classification began to be developed in the Soviet Union as early as 1920–1930th in the papers by L. G. Ramenskiy who in 1927 published the definition of habitat type: The type of habitat or natural area is determined by a combination of climate conditions, relief, irrigation, and the nature of the soil and subsoil. The same type can be covered by a meadow, or a forest, or plowed up, etc.: these are its transitional states (in virgin untouched nature, each type is inhabited by a completely definite combination of plants - steppe, forest, meadow, etc.). Afterwards L. G. Ramenskiy began to use the term land type instead of habitat type. In the 1930s, by the land type he meant an ecosystem unit in which plant community would exist without human influence. The land type in nature is represented by a set of various modifications that arise, as a rule, under man pressure. Modifications can transform into each other and revert to the original state of the type. Later, such plant community was called potential vegetation (Tüxen, 1956). In 1932–1935, L. G. Ramenskiy supervised the inventory of natural forage lands in the USSR, which used this concept of land type (Golub, 2015). The inventory of natural forage lands in the USSR resulted in their hierarchical classification: 19 classes and 43 subclasses were established. The exact number of distinguished types was not calculated, according to L. G. Ramenskiy rough assessment, there were more than thousand. In most cases, the potential vegetation of the types could not be identified. Proceedings of this inventory were not published. However, the L. G. Ramenskiy former post-graduate student N. V. Kuksin, who took part in the inventory in Ukraine, wrote the book about the forage type lands in this republic of the USSR (Kuksin, 1935). The typology of hayfields and pastures presented in that book is very similar to the habitat classification developed on the principles of the EUNIS system (Kuzemko et al., 2018). By the late 1940s, L. G. Ramenskiy had concluded that modern science was unable to establish potential vegetation for many habitat types. Therefore, he recommended calling the land type what he previously attributed to modifications. For practical reasons and for the sake of brevity, it is advisable to also call types the main groups of modifications of land types (forest, meadow, arable) (Ramenskiy, 1950, p. 489). As a result, his understanding of land type became the same as later habitat was interpreted in the EUNIS system. The typology by L. G. Ramenskiy lands and the classification of EUNIS habitats have the same essence and basis, but different groups of human society proposed them: the first exploits land resources, the second tries to protect them. Based on L. G. Ramenskiy typology, recommendations are made on the use of biotopes with the purpose to obtain sustainable maximum economic production. Based on the classification of the EUNIS system, recommendations are drawn up for the protection of plant and animal populations, as well as their community’s characteristic of a given biotope. The land typology by L. G. Ramenskiy could well be deployed towards the protection of biotopes, if there was a demand from society for such use. So keen interest in nature conservation, as now, did not exist in the course of the L. G. Ramenskiy lifetime. At present, the EUNIS biotope classification has begun to be used on the territory of the former USSR, while the land typology by L. G. Ramenskiy has been forgotten. There are two reasons for this phenomenon: 1) isolationism of Soviet science, which separated domestic scientists from their colleagues in the West; 2) L. G. Ramenskiy ideas were too ahead of time, their depth, essence and importance became understandable to biologists only few decades later. The paper shows that the formation of L. G. Ramenskiy views concerning the typology of habitats could been influenced by the ideas of the Russian forest scientist A. A. Krudener.

Perceptions of land managers towards using hybrid and genetically modified trees

Hybridization and genetic modification are potential methods for incorporation of stress tolerance being explored to support some at-risk tree species. However, many concerns, both ecological and economic, have been identified in using these biotechnologies, such as potential for invasiveness or high cost. There is limited information on perceptions towards hybrid and genetically modified (GM) trees, particularly from individuals responsible for widescale tree management. An online survey was administered to land managers in Indiana, USA to gauge perceptions to hybrid and GM trees, and current hybrid tree use. Land managers had stronger concern for ecological, rather than economic, issues, with potential for invasiveness being strongest. Agreement was highest for using the tree types for conservation and restoration of at-risk species and production purposes. However, perceptions varied by characteristics such as concern type, age, and land type managed. Ecological concern and land type managed most strongly predicted hybrid use. Overall, the majority of land managers agreed, rather than disagreed, with a variety of potential hybrid and GM tree advantages and purposes. Thus, results indicate that if these biotechnologies are deemed appropriate for supporting at-risk species, the majority of land managers in Indiana might be agreeable to such recommendations from researchers. However, it was also clear that despite this, most respondents concurrently had strong ecological concerns about suitability as a native species replacement. To address this, it is essential that these tree types be thoroughly vetted, and land managers be actively engaged in the process, as this population would ultimately be responsible for any widescale implementation of hybrid and GM trees.